import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowerCamelCase__  =					logging.get_logger(__name__)


lowerCamelCase__  =					{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
lowerCamelCase__  =					{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase__  =					{
    '''allenai/led-base-16384''': 1_6384,
}


@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 				lowerCAmelCase__       (			)      ->List[str]:




  '''simple docstring'''

  _UpperCamelCase           =    (
      list(range(ord("!" )    ,     ord("~" ) + 1 ) ) + list(range(ord("¡" )    ,     ord("¬" ) + 1 ) ) + list(range(ord("®" )    ,     ord("ÿ" ) + 1 ) )
  )
  _UpperCamelCase           =    bs[:]
  _UpperCamelCase           =    0
  for b in range(2**8 ):
    if b not in bs:
      bs.append(a__ )
      cs.append(2**8 + n )
      n += 1
  _UpperCamelCase           =    [chr(a__ ) for n in cs]
  return dict(zip(a__    ,     a__ ) )


def 				lowerCAmelCase__       (			a__ )      ->Tuple:




  '''simple docstring'''

  _UpperCamelCase           =    set()
  _UpperCamelCase           =    word[0]
  for char in word[1:]:
    pairs.add((prev_char, char) )
    _UpperCamelCase           =    char
  return pairs







class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					VOCAB_FILES_NAMES
 __A			       =					PRETRAINED_VOCAB_FILES_MAP
 __A			       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __A			       =					['''input_ids''', '''attention_mask''']







 def __init__(		self				:   Any							, lowercase_				:   str							, lowercase_				:   int							, lowercase_				:   List[Any]="replace"							, lowercase_				:   List[str]="<s>"							, lowercase_				:   Tuple="</s>"							, lowercase_				:   Tuple="</s>"							, lowercase_				:   Union[str, Any]="<s>"							, lowercase_				:   Tuple="<unk>"							, lowercase_				:   List[Any]="<pad>"							, lowercase_				:   Any="<mask>"							, lowercase_				:   int=False							, **lowercase_				:   Tuple							, )						->	int:


   """simple docstring"""







   _UpperCamelCase           =    AddedToken(lowercase_							, lstrip=lowercase_							, rstrip=lowercase_) if isinstance(lowercase_							, lowercase_) else bos_token
   _UpperCamelCase           =    AddedToken(lowercase_							, lstrip=lowercase_							, rstrip=lowercase_) if isinstance(lowercase_							, lowercase_) else eos_token
   _UpperCamelCase           =    AddedToken(lowercase_							, lstrip=lowercase_							, rstrip=lowercase_) if isinstance(lowercase_							, lowercase_) else sep_token
   _UpperCamelCase           =    AddedToken(lowercase_							, lstrip=lowercase_							, rstrip=lowercase_) if isinstance(lowercase_							, lowercase_) else cls_token
   _UpperCamelCase           =    AddedToken(lowercase_							, lstrip=lowercase_							, rstrip=lowercase_) if isinstance(lowercase_							, lowercase_) else unk_token
   _UpperCamelCase           =    AddedToken(lowercase_							, lstrip=lowercase_							, rstrip=lowercase_) if isinstance(lowercase_							, lowercase_) else pad_token

   # Mask token behave like a normal word, i.e. include the space before it
   _UpperCamelCase           =    AddedToken(lowercase_							, lstrip=lowercase_							, rstrip=lowercase_) if isinstance(lowercase_							, lowercase_) else mask_token

   super().__init__(
       errors=lowercase_							, bos_token=lowercase_							, eos_token=lowercase_							, unk_token=lowercase_							, sep_token=lowercase_							, cls_token=lowercase_							, pad_token=lowercase_							, mask_token=lowercase_							, add_prefix_space=lowercase_							, **lowercase_							, )

   with open(lowercase_							, encoding="utf-8") as vocab_handle:
     _UpperCamelCase           =    json.load(lowercase_)
   _UpperCamelCase           =    {v: k for k, v in self.encoder.items()}
   _UpperCamelCase           =    errors  # how to handle errors in decoding
   _UpperCamelCase           =    bytes_to_unicode()
   _UpperCamelCase           =    {v: k for k, v in self.byte_encoder.items()}
   with open(lowercase_							, encoding="utf-8") as merges_handle:
     _UpperCamelCase           =    merges_handle.read().split("\n")[1:-1]
   _UpperCamelCase           =    [tuple(merge.split()) for merge in bpe_merges]
   _UpperCamelCase           =    dict(zip(lowercase_							, range(len(lowercase_))))
   _UpperCamelCase           =    {}
   _UpperCamelCase           =    add_prefix_space

   # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
   _UpperCamelCase           =    re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+")







 @property
 # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
 def 		__UpperCAmelCase				(		self				:   str)						->	List[str]:


   """simple docstring"""







   return len(self.encoder)







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	List[Any]:


   """simple docstring"""







   return dict(self.encoder							, **self.added_tokens_encoder)







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   List[Any])						->	Optional[Any]:


   """simple docstring"""







   if token in self.cache:
     return self.cache[token]
   _UpperCamelCase           =    tuple(lowercase_)
   _UpperCamelCase           =    get_pairs(lowercase_)

   if not pairs:
     return token

   while True:
     _UpperCamelCase           =    min(lowercase_							, key=lambda lowercase_: self.bpe_ranks.get(lowercase_							, float("inf")))
     if bigram not in self.bpe_ranks:
       break
     _UpperCamelCase      ,						_UpperCamelCase           =    bigram
     _UpperCamelCase           =    []
     _UpperCamelCase           =    0
     while i < len(lowercase_):
       try:
         _UpperCamelCase           =    word.index(lowercase_							, lowercase_)
       except ValueError:
         new_word.extend(word[i:])
         break
       else:
         new_word.extend(word[i:j])
         _UpperCamelCase           =    j

       if word[i] == first and i < len(lowercase_) - 1 and word[i + 1] == second:
         new_word.append(first + second)
         i += 2
       else:
         new_word.append(word[i])
         i += 1
     _UpperCamelCase           =    tuple(lowercase_)
     _UpperCamelCase           =    new_word
     if len(lowercase_) == 1:
       break
     else:
       _UpperCamelCase           =    get_pairs(lowercase_)
   _UpperCamelCase           =    " ".join(lowercase_)
   _UpperCamelCase           =    word
   return word







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   List[Any])						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    []
   for token in re.findall(self.pat							, lowercase_):
     _UpperCamelCase           =    "".join(
         self.byte_encoder[b] for b in token.encode("utf-8"))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
     bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase_).split(" "))
   return bpe_tokens







 def 		__UpperCAmelCase				(		self				:   Dict							, lowercase_				:   List[str])						->	Dict:


   """simple docstring"""







   return self.encoder.get(lowercase_							, self.encoder.get(self.unk_token))







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   List[Any])						->	List[Any]:


   """simple docstring"""







   return self.decoder.get(lowercase_)







 def 		__UpperCAmelCase				(		self				:   Dict							, lowercase_				:   int)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    "".join(lowercase_)
   _UpperCamelCase           =    bytearray([self.byte_decoder[c] for c in text]).decode("utf-8"							, errors=self.errors)
   return text







 def 		__UpperCAmelCase				(		self				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   Optional[str] = None)						->	Tuple[str]:


   """simple docstring"""







   if not os.path.isdir(lowercase_):
     logger.error(f'Vocabulary path ({save_directory}) should be a directory')
     return
   _UpperCamelCase           =    os.path.join(
       lowercase_							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
   _UpperCamelCase           =    os.path.join(
       lowercase_							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"])

   with open(lowercase_							, "w"							, encoding="utf-8") as f:
     f.write(json.dumps(self.encoder							, indent=2							, sort_keys=lowercase_							, ensure_ascii=lowercase_) + "\n")

   _UpperCamelCase           =    0
   with open(lowercase_							, "w"							, encoding="utf-8") as writer:
     writer.write("#version: 0.2\n")
     for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							, key=lambda lowercase_: kv[1]):
       if index != token_index:
         logger.warning(
             f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
             " Please check that the tokenizer is not corrupted!")
         _UpperCamelCase           =    token_index
       writer.write(" ".join(lowercase_) + "\n")
       index += 1

   return vocab_file, merge_file







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   List[int]							, lowercase_				:   Optional[List[int]] = None)						->	List[int]:


   """simple docstring"""







   if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   _UpperCamelCase           =    [self.cls_token_id]
   _UpperCamelCase           =    [self.sep_token_id]
   return cls + token_ids_a + sep + sep + token_ids_a + sep







 def 		__UpperCAmelCase				(		self				:   str							, lowercase_				:   List[int]							, lowercase_				:   Optional[List[int]] = None							, lowercase_				:   bool = False)						->	List[int]:


   """simple docstring"""







   if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=lowercase_							, token_ids_a=lowercase_							, already_has_special_tokens=lowercase_)

   if token_ids_a is None:
     return [1] + ([0] * len(lowercase_)) + [1]
   return [1] + ([0] * len(lowercase_)) + [1, 1] + ([0] * len(lowercase_)) + [1]







 def 		__UpperCAmelCase				(		self				:   Dict							, lowercase_				:   List[int]							, lowercase_				:   Optional[List[int]] = None)						->	List[int]:


   """simple docstring"""







   _UpperCamelCase           =    [self.sep_token_id]
   _UpperCamelCase           =    [self.cls_token_id]

   if token_ids_a is None:
     return len(cls + token_ids_a + sep) * [0]
   return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]







 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   Tuple							, lowercase_				:   Union[str, Any]=False							, **lowercase_				:   Any)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    kwargs.pop("add_prefix_space"							, self.add_prefix_space)
   if (is_split_into_words or add_prefix_space) and (len(lowercase_) > 0 and not text[0].isspace()):
     _UpperCamelCase           =    " " + text
   return (text, kwargs)







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Union[Dict[str, EncodedInput], BatchEncoding]							, lowercase_				:   Optional[int] = None							, lowercase_				:   PaddingStrategy = PaddingStrategy.DO_NOT_PAD							, lowercase_				:   Optional[int] = None							, lowercase_				:   Optional[bool] = None							, )						->	dict:


   """simple docstring"""







   _UpperCamelCase           =    super()._pad(
       encoded_inputs=lowercase_							, max_length=lowercase_							, padding_strategy=lowercase_							, pad_to_multiple_of=lowercase_							, return_attention_mask=lowercase_							, )

   # Load from model defaults
   if return_attention_mask is None:
     _UpperCamelCase           =    "attention_mask" in self.model_input_names

   if return_attention_mask and "global_attention_mask" in encoded_inputs:
     _UpperCamelCase           =    encoded_inputs[self.model_input_names[0]]
     # `global_attention_mask` need to have the same length as other (sequential) inputs.
     _UpperCamelCase           =    len(encoded_inputs["global_attention_mask"]) != len(lowercase_)

     if needs_to_be_padded:
       _UpperCamelCase           =    len(lowercase_) - len(encoded_inputs["global_attention_mask"])

       if self.padding_side == "right":
         # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
         _UpperCamelCase           =    (
             encoded_inputs["global_attention_mask"] + [-1] * difference
         )
       elif self.padding_side == "left":
         _UpperCamelCase           =    [-1] * difference + encoded_inputs[
             "global_attention_mask"
         ]
       else:
         raise ValueError("Invalid padding strategy:" + str(self.padding_side))

   return encoded_inputs





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
   from .pipeline_kandinsky import KandinskyPipeline
   from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
   from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
   from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
   from .text_encoder import MultilingualCLIP




